A Blind Deconvolution Algorithm using Zernike-polynomial-based Phase Fitting
Authors: 
Zhilei Ren, Jin Liu, Yonghui LiangAuthors Info & Claims
Zhilei Ren, Jin Liu, Yonghui LiangAuthors Info & ClaimsPages 54 - 58
Abstract
The correction of an adaptive optics (AO) system is always partially, a post-processing technique such as blind deconvolution can be used to improve the image quality. In this paper, we propose a blind deconvolution algorithm using Zernike-polynomial-based phase fitting to recover a high resolution and contrast image from the short-exposure AO images. The strategy is to parameterize the point spread function (PSF) with phase aberrations pixel by pixel and introduce a constraint to the phase estimations using phase fitting. The algorithm is robust and can lead to a restoration free of artifacts. We evaluate the performance of our proposed algorithm by a number of simulated experiments and it is well performed.
References
[1]
Conan, J. M., Mugnier, L. M., and Fusco T. 1998. Myopic deconvolution of adaptive optics images by use of object and point-spread function power spectra. Applied Optics, 37, 21, 4614.
[2]
Hope, D. A., Jefferies, S. M., and Hart M. 2016. High-resolution speckle imaging through strong atmospheric turbulence. Optics express, 24, 11, 12116--12129.
[3]
Li, D., Sun, C., and Yang, J. 2017. Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics. Sensors, 17, 4, 785.
[4]
Hao, Z., Yu, L., and Wu, Q. 2007. Blind image deconvolution subject to bandwidth and total variation constraints. Optics Letters, 32, 17, 2550.
[5]
Mugnier, L. M., Robert, C., and Conan, J. M. 2001. Myopic deconvolution from wave-front sensing. Josa A, 18, 4, 862--872.
[6]
Rudin, L. I., Osher, S., and Fatemi, E. 1992. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60. 1-4, 259--268.
[7]
Matson, C. L., Borelli, K., and Jefferies, S. 2009. Fast and optimal multiframe blind deconvolution algorithm for high-resolution ground-based imaging of space objects. Applied Optics, 48, 1, 75--92.
[8]
Fortran, I., Press, W. H., and Teukolsky, S. A. 1992. Numerical Recipes. Cambridge university press.
[9]
Mugnier, L. M., Robert, C., and Conan, J. M. 2001 Myopic deconvolution from wave-front sensing. Josa A, 18, 4, 862--872.
[10]
Leung, V., Lane, R. G. 2000. Blind deconvolution of images blurred by atmospheric speckle. //Image Reconstruction from Incomplete Data. International Society for Optics and Photonics, 4123, 73--84.
[11]
Liao, H., Li, F., Ng, M. K. 2009. Selection of regularization parameter in total variation image restoration. Journal of the Optical Society of America A Optics Image Science & Vision, 26, 11, 2311.
[12]
Mugnier, L. M., Fusco, T., and Conan, J. M. 2004. Mistral: a myopic edge-preserving image restoration method, with application to astronomical adaptive-optics-corrected long-exposure images. Journal of the Optical Society of America A Optics Image Science & Vision, 21, 10, 1841--54.
[13]
Noll, R. J. 1976. Zernike polynomials and atmospheric turbulence. Journal of the Optical Society of America, 66, 3, 207--211.
[14]
Schmidt, J. D. 2010. Numverical Simulation of Optical Wave Propagation with Examples in MATLAB. SPIE.
Index Terms
- A Blind Deconvolution Algorithm using Zernike-polynomial-based Phase Fitting
Recommendations
Two-dimensional blind deconvolution using a robust GCD approach
ICIP '97: Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1We examine the applicability of the previously proposed greatest common divisor (GCD) method to blind image deconvolution. In this method, the desired image is approximated as the GCD of the two-dimensional polynomials corresponding to the z-transforms ...
Multichannel blind signal deconvolution using high order statistics
SSAP '96: Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)The problem of multichannel blind signal deconvolution is considered. We show that input signals can be restored (or separated) using only the condition that they are statistically independent. Two main necessary and sufficient conditions involving high ...
A simulated annealing version of the EM algorithm for non-Gaussian deconvolution
The Expectation–Maximization (EM) algorithm is a very popular technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed form, a stochastic approximation of EM (SAEM) can be used. Under ...
Comments
Information & Contributors
Information
Published In
June 2018
261 pages
ISBN:9781450364607
DOI:10.1145/3239576
Copyright © 2018 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
- Southwest Jiaotong University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 June 2018
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICAIP '18
ICAIP '18: 2018 the 2nd International Conference on Advances in Image Processing
June 16 - 18, 2018
Chengdu, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 101Total Downloads
- Downloads (Last 12 months)18
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in